A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization

被引：3
|
作者：
Che, Hangjun [1 ,2 ]
Wang, Jun [1 ,2 ]
机构：
[1] City Univ Hong Kong, Dept Comp Sci, Kowloon Tong, Hong Kong, Peoples R China
[2] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Peoples R China
来源：
NEURAL INFORMATION PROCESSING (ICONIP 2018), PT II | 2018年 / 11302卷
基金：
中国国家自然科学基金;
关键词：
Symmetric nonnegative matrix factorization; Collaborative neurodynamic approach; Augmented Lagrangian function;
D O I：
10.1007/978-3-030-04179-3_40
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a collaborative neurodynamic approach to symmetric nonnegative matrix factorization (SNMF). First, a formulated nonconvex optimization problem of SNMF is described. To solve this problem, a neurodynamic model based on an augmented Lagrangian function is proposed and proven to be convergent to a strict local optimal solution under the second-order sufficiency condition. Next, a group of neurodynamic models are employed to search for an optimal factorized matrix by using particle swarm algorithm to update the initial neuronal states. The efficacy of the proposed approach is substantiated on two datasets.
引用
收藏
页码：453 / 462
页数：10
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